Hail is…

  • a library for analyzing structured tabular and matrix data
  • a collection of primitives for operating on data in parallel
  • a suite of functionality for processing genetic data
  • not an acronym


In Python, 5 is of type int while "hello" is of type str. Python is a dynamically-typed language, meaning that a function like:

>>> def add_x_and_y(x, y):
...     return x + y

can be called on any two objects which can be added, like numbers, strings, or numpy arrays.

Types are very important in Hail, because the fields of Table and MatrixTable objects have data types.

Hail has basic data types for numeric and string objects:

  • tstr - Text string.
  • tbool - Boolean (True or False) value.
  • tint32 - 32-bit integer.
  • tint64 - 64-bit integer.
  • tfloat32 - 32-bit floating point number.
  • tfloat64 - 64-bit floating point number.

Hail has genetics-specific types:

  • tcall - Genotype calls.
  • tlocus - Genomic locus, parameterized by reference genome.

Hail has container types:

  • tarray - Ordered collection of homogenous objects.
  • tset - Unordered collection of distinct homogenous objects.
  • tdict - Key-value map. Keys and values are both homogenous.
  • ttuple - Tuple of heterogeneous values.
  • tstruct - Structure containing named fields, each with its own type.

Homogenous collections are a change from standard Python collections. While the list ['1', 2, 3.0] is a perfectly valid Python list, a Hail array could not contain both tstr and tint32 objects. Likewise, a the dict {'a': 1, 2: 'b'} is a valid Python dictionary, but a Hail dictionary cannot contain keys of different types. An example of a valid dictionary is {'a': 1, 'b': 2}, where the keys are all strings and the values are all integers. The type of this dictionary would be dict<str, int32>.

The tstruct type is used to compose types together to form nested structures. The tstruct is an ordered mapping from field name to field type. Each field name must be unique.


The Python language allows users to specify their computations using expressions. For example, a simple expression is 5 + 6. This will be evaluated and return 11. You can also assign expressions to variables and then add variable expressions together such as x = 5; y = 6; x + y.

Throughout Hail documentation and tutorials, you will see Python code like this:

>>> ht2 = ht.annotate(C4 = ht.C3 + 3 * ht.C2 ** 2)

However, Hail is not running Python code on your data. Instead, Hail is keeping track of the computations applied to your data, then compiling these computations into native code and running them in parallel.

This happens using the Expression class. Hail expressions operate much like Python objects of the same type: for example, an Int32Expression can be used in arithmetic with other integers or expressions in much the same way a Python int can. However, you will be unable to use these expressions with other modules, like numpy or scipy.

Expression objects keep track of their data type. This can be accessed with Expression.dtype():

>>> i = hl.int32(100)
>>> i.dtype

The Hail equivalent of the Python example above would be as follows:

>>> x = hl.int32(5)
>>> y = hl.int32(6)

We can print x in a Python interpreter and see that x is an Int32Expression. This makes sense because x is a Python int.

>>> x
<Int32Expression of type int32>

We can add two Int32Expression objects together just like with Python int objects. x + y returns another Int32Expression representing the computation of x + y and not an actual value.

>>> z = x + y
>>> z
<Int32Expression of type int32>

To peek at the value of this computation, there are two options: Expression.value(), which returns a Python value, and Expression.show(), which prints a human-readable representation of an expression.

>>> z.value
>>> z.show()
| <expr> |
|  int32 |
|     11 |

Expressions like to bring Python objects into the world of expressions as well. For example, we can add a Python int to an Int32Expression.

>>> x + 3
<Int32Expression of type int32>

Addition is commutative, so we can also add an Int32Expression to an int.

>>> 3 + x
<Int32Expression of type int32>

Hail has many subclasses of Expression – one for each Hail type. Each subclass defines possible methods and operations that can be applied. For example, if we have a list of Python integers, we can convert this to a Hail ArrayNumericExpression with either array() or literal():

>>> a = hl.array([1, 2, -3, 0, 5])
>>> a
<ArrayNumericExpression of type array<int32>>
>>> a.dtype

Hail arrays can be indexed and sliced like Python lists or numpy arrays:

>>> a[1]
>>> a[1:-1]

Boolean Logic

Unlike Python, a Hail BooleanExpression cannot be used with and, or, and not. The equivalents are &, |, and ~.

>>> s1 = x == 3
>>> s2 = x != 4
>>> s1 & s2 # s1 and s2
>>> s1 | s2 # s1 or s2
>>> ~s1 # not s1


The operator precedence of & and | is different from and and or. You will need parentheses around expressions like this:

>>> (x == 3) & (x != 4)


Python if / else do not work with Hail expressions. Instead, you must use the cond(), case(), and switch() functions.

A conditional expression has three components: the condition to evaluate, the consequent value to return if the condition is True, and the alternate to return if the condition is False. For example:

if (x > 0):
    return 1
    return 0

In the above conditional, the condition is x > 0, the consequent is 1, and the alternate is 0.

Here is the Hail expression equivalent with cond():

>>> hl.cond(x > 0, 1, 0)
 <Int32Expression of type int32>

This example returns an Int32Expression which can be used in more computations:

>>> a + hl.cond(x > 0, 1, 0)
<ArrayNumericExpression of type array<int32>>

More complicated conditional statements can be constructed with case(). For example, we might want to emit 1 if x < -1, 2 if -1 <= x <= 2 and 3 if x > 2.

>>> (hl.case()
...   .when(x < -1, 1)
...   .when((x >= -1) & (x <= 2), 2)
...   .when(x > 2, 3)
...   .or_missing())
<Int32Expression of type int32>

Finally, Hail has the switch() function to build a conditional tree based on the value of an expression. In the example below, csq is a StringExpression representing the functional consequence of a mutation. If csq does not match one of the cases specified by SwitchBuilder.when(), it is set to missing with SwitchBuilder.or_missing(). Other switch statements are documented in the SwitchBuilder class.

>>> csq = hl.str('nonsense')
>>> (hl.switch(csq)
...    .when("synonymous", False)
...    .when("intron", False)
...    .when("nonsense", True)
...    .when("indel", True)
...    .or_missing())
<BooleanExpression of type bool>


In Hail, all expressions can be missing. An expression representing a missing value of a given type can be generated with the null() function, which takes the type as its single argument. An example of generating a Float64Expression that is missing is:

>>> hl.null('float64')

These can be used with conditional statements to set values to missing if they don’t satisfy a condition:

>>> hl.cond(x > 2.0, x, hl.null(hl.tfloat))

The result of method calls on a missing value is None. For example, if we define cnull to be a missing value with type tcall, calling the method is_het will return None and not False.

>>> cnull = hl.null('call')
>>> cnull.is_het().value

Binding Variables

Hail inlines function calls each time an expression appears. This can result in unexpected behavior when random values are used. For example, let x be a random number generated with the function rand_unif():

>>> x = hl.rand_unif(0, 1)

The value of x changes with each evaluation:

>>> x.value
>>> x.value

If we create a list with x repeated 3 times, we’d expect to get an array with identical values. However, instead we see a list of 3 random numbers.

>>> hl.array([x, x, x]).value
[0.8846327207915881, 0.14415148553468504, 0.8202677741734825]

To solve this problem, we can use the bind() function to bind an expression to a value before applying it in a function.

>>> expr = hl.bind(lambda x: [x, x, x], hl.rand_unif(0, 1))
>>> expr.value
[0.5562065047992025, 0.5562065047992025, 0.5562065047992025]


In addition to the methods exposed on each Expression, Hail also has numerous functions that can be applied to expressions, which also return an expression.

Take a look at the Functions page for full documentation.


A Table is the Hail equivalent of a SQL table, a Pandas Dataframe, an R Dataframe, a dyplr Tibble, or a Spark Dataframe. It consists of rows of data conforming to a given schema where each column (row field) in the dataset is of a specific type.


Hail has functions to create tables from a variety of data sources. The most common use case is to load data from a TSV or CSV file, which can be done with the import_table() function.

ht = hl.import_table(“data/kt_example1.tsv”, impute=True)

Examples of genetics-specific import methods are import_locus_intervals(), import_fam(), and import_bed(). Many Hail methods also return tables.

An example of a table is below. We recommend ht as a variable name for tables, referring to a “Hail table”.

>>> ht.show()
|    ID |    HT | SEX |     X |     Z |    C1 |    C2 |    C3 |
| int32 | int32 | str | int32 | int32 | int32 | int32 | int32 |
|     1 |    65 | M   |     5 |     4 |     2 |    50 |     5 |
|     2 |    72 | M   |     6 |     3 |     2 |    61 |     1 |
|     3 |    70 | F   |     7 |     3 |    10 |    81 |    -5 |
|     4 |    60 | F   |     8 |     2 |    11 |    90 |   -10 |

Global Fields

In addition to row fields, Hail tables also have global fields. You can think of globals as extra fields in the table whose values are identical for every row. For example, the same table above with the global field G = 5 can be thought of as

|    ID |    HT | SEX |     X |     Z |    C1 |    C2 |    C3 |     G |
| int32 | int32 | str | int32 | int32 | int32 | int32 | int32 | int32 |
|     1 |    65 | M   |     5 |     4 |     2 |    50 |     5 |     5 |
|     2 |    72 | M   |     6 |     3 |     2 |    61 |     1 |     5 |
|     3 |    70 | F   |     7 |     3 |    10 |    81 |    -5 |     5 |
|     4 |    60 | F   |     8 |     2 |    11 |    90 |   -10 |     5 |

but the value 5 is only stored once for the entire dataset and NOT once per row of the table. The output of Table.describe() lists what all of the row fields and global fields are.

>>> ht.describe()
Global fields:
Row fields:
    'ID': int32
    'HT': int32
    'SEX': str
    'X': int32
    'Z': int32
    'C1': int32
    'C2': int32
    'C3': int32


Row fields can be specified to be the key of the table with the method Table.key_by(). Keys are important for joining tables together (discussed below).

Referencing Fields

Each Table object has all of its row fields and global fields as attributes in its namespace. This means that the row field ID can be accessed from table ht with ht.Sample or ht['Sample']. If ht also had a global field G, then it could be accessed by either ht.G or ht['G']. Both row fields and global fields are top level fields. Be aware that accessing a field with the dot notation will not work if the field name has spaces or special characters in it. The Python type of each attribute is an Expression that also contains context about its type and source, in this case a row field of table ht.

>>> ht
<hail.table.Table at 0x110791a20>
>>> ht.ID
<Int32Expression of type int32>

Common Operations

The main operations on a table are Table.select() and Table.drop() to add or remove row fields, Table.filter() to either keep or remove rows based on a condition, and Table.annotate() to add new row fields or update the values of existing row fields. For example:

>>> ht_new = ht.filter(ht['C1'] >= 10)
>>> ht_new = ht_new.annotate(id_times_2 = ht_new.ID * 2)


A commonly used operation is to compute an aggregate statistic over the rows of the dataset. Hail provides an Table.aggregate() method along with many aggregator functions (see Aggregators) to return the result of a query:

>>> ht.aggregate(agg.fraction(ht.SEX == 'F'))

We also might want to compute the mean value of HT for each sex. This is possible with a combination of Table.group_by() and GroupedTable.aggregate():

>>> ht_agg = (ht.group_by(ht.SEX)
...             .aggregate(mean = agg.mean(ht.HT)))
>>> ht_agg.show()
| SEX |        mean |
| str |     float64 |
| M   | 6.85000e+01 |
| F   | 6.50000e+01 |

Note that the result of ht.group_by(...).aggregate(...) is a new Table while the result of ht.aggregate(...) is a Python value.


To join the row fields of two tables together, Hail provides a Table.join() method with options for how to join the rows together (left, right, inner, outer). The tables are joined by the row fields designated as keys. The number of keys and their types must be identical between the two tables. However, the names of the keys do not need to be identical. Use the Table.key attribute to view the current table row keys and the Table.key_by() method to change the table keys. If top level row field names overlap between the two tables, the second table’s field names will be appended with a unique identifier “_N”.

>>> ht = ht.key_by('ID')
>>> ht2 = hl.import_table("data/kt_example2.tsv", impute=True).key_by('ID')
>>> ht_join = ht.join(ht2)
>>> ht_join.show()
|    ID |    HT | SEX |     X |     Z |    C1 |    C2 |    C3 |     A | B      |
| int32 | int32 | str | int32 | int32 | int32 | int32 | int32 | int32 | str    |
|     3 |    70 | F   |     7 |     3 |    10 |    81 |    -5 |    70 | mouse  |
|     4 |    60 | F   |     8 |     2 |    11 |    90 |   -10 |    60 | rabbit |
|     2 |    72 | M   |     6 |     3 |     2 |    61 |     1 |    72 | dog    |
|     1 |    65 | M   |     5 |     4 |     2 |    50 |     5 |    65 | cat    |

In addition to using the Table.join() method, Hail provides an additional join syntax using Python’s bracket notation. This syntax does a left join, like looking up values in a dictionary. Instead of returning a Table, this syntax returns an Expression which can be used in expressions of the left table. For example, below we add the field ‘B’ from ht2 to ht:

>>> ht1 = ht.annotate(B = ht2[ht.ID].B)
>>> ht1.show()
|    ID |    HT | SEX |     X |     Z |    C1 |    C2 |    C3 | B      |
| int32 | int32 | str | int32 | int32 | int32 | int32 | int32 | str    |
|     3 |    70 | F   |     7 |     3 |    10 |    81 |    -5 | mouse  |
|     4 |    60 | F   |     8 |     2 |    11 |    90 |   -10 | rabbit |
|     2 |    72 | M   |     6 |     3 |     2 |    61 |     1 | dog    |
|     1 |    65 | M   |     5 |     4 |     2 |    50 |     5 | cat    |

Interacting with Tables Locally

Hail has many useful methods for interacting with tables locally such as in an Jupyter notebook. Use the Table.show() method to see the first few rows of a table.

Table.take() will collect the first n rows of a table into a local Python list:

>>> first3 = ht.take(3)
>>> first3
[Struct(ID=3, HT=70, SEX=F, X=7, Z=3, C1=10, C2=81, C3=-5),
 Struct(ID=4, HT=60, SEX=F, X=8, Z=2, C1=11, C2=90, C3=-10),
 Struct(ID=2, HT=72, SEX=M, X=6, Z=3, C1=2, C2=61, C3=1)]

Note that each element of the list is a Struct whose elements can be accessed using Python’s get attribute or get item notation:

>>> first3[0].ID
>>> first3[0]['ID']

The Table.head() method is helpful for testing pipelines. It subsets a table to the first n rows, causing downstream operations to run much more quickly.

Table.describe() is a useful method for showing all of the fields of the table and their types. The types themselves can be accessed using the fields (e.g. ht.ID.dtype), and the full row and global types can be accessed with ht.row.dtype and ht.globals.dtype. The row fields that are part of the key can be accessed with Table.key. The Table.count() method returns the number of rows.


Hail provides multiple methods to export data to other formats. Tables can be exported to TSV files with the Table.export() method or written to disk in Hail’s on-disk format with Table.write() (these files may be read in with read_table()). Tables can also be exported to pandas DataFrames with Table.to_pandas() or to pyspark Dataframes with Table.to_spark().


A MatrixTable is a distributed two-dimensional dataset consisting of four components: a two-dimensional matrix where each entry is indexed by row key(s) and column key(s), a corresponding rows table that stores all of the row fields which are constant for every column in the dataset, a corresponding columns table that stores all of the column fields that are constant for every row in the dataset, and a set of global fields that are constant for every entry in the dataset.

Unlike a Table which has two field groups (row fields and global fields), a matrix table has four field groups: global fields, row fields, column fields, entry fields.

In addition, there are different operations on the matrix for each field group. For instance, Table has Table.select() and Table.select_globals(), and MatrixTable has MatrixTable.select_rows(), MatrixTable.select_cols(), MatrixTable.select_entries(), and MatrixTable.select_globals().

It is possible to represent matrix data by coordinate in a table , storing one record per entry of the matrix. However, the MatrixTable represents this data far more efficiently and exposes natural interfaces for computing on it.

The MatrixTable.rows() and MatrixTable.cols() methods return the row and column fields as separate tables. The MatrixTable.entries() method returns the matrix as a table in coordinate form – use this object with caution.


Matrix tables have keys just as tables do. However, instead of one key, matrix tables have two keys: a row key and a column key. Row fields are indexed by the row key, column fields are indexed by the column key, and entry fields are indexed by the row key and the column key. The key structs can be accessed with MatrixTable.row_key and MatrixTable.col_key. It is possible to change the key with MatrixTable.key_rows_by() and MatrixTable.key_cols_by().

Note that changing the row key, however, may be an expensive operation.

Hail matrix tables are natively distributed objects, and as such have another key: a partition key. This key is used for specifying the ordering of the matrix table along the row dimension, which is important for performance. Access this with MatrixTable.partition_key

Referencing Fields

All fields (row, column, global, entry) are top-level and exposed as attributes on the MatrixTable object. For example, if the matrix table mt had a row field locus, this field could be referenced with either mt.locus or mt['locus']. The former access pattern does not work with field names with spaces or punctuation.

The result of referencing a field from a matrix table is an Expression which knows its type and knows its source as well as whether it is a row field, column field, entry field, or global field. Hail uses this context to know which operations are allowed for a given expression.

When evaluated in a Python interpreter, we can see mt.locus is a LocusExpression with type locus<GRCh37> and it is a row field of the MatrixTable mt.

>>> mt
<hail.matrixtable.MatrixTable at 0x1107e54a8>
>>> mt.locus
<LocusExpression of type locus<GRCh37>>

Likewise, mt.DP would be an Int32Expression with type int32 and is an entry field of mt. It is indexed by both rows and columns as denoted by its indices when describing the expression:

>>> mt.DP.describe()
    <class 'hail.matrixtable.MatrixTable'>
    ['row', 'column']


Text files may be imported with import_matrix_table(). Additionally, Hail provides functions to import genetic datasets as matrix tables from a variety of file formats: import_vcf(), import_plink(), import_bgen(), and import_gen().

>>> mt = hl.import_vcf('data/sample.vcf.bgz')

The MatrixTable.describe() method prints all fields in the table and their types, as well as the keys.

>>> mt.describe()
Global fields:
Column fields:
    's': str
Row fields:
    'locus': locus<GRCh37>
    'alleles': array<str>
    'rsid': str
    'qual': float64
    'filters': set<str>
    'info': struct {
        NEGATIVE_TRAIN_SITE: bool,
        AC: array<int32>,
        DS: bool
Entry fields:
    'GT': call
    'AD': array<int32>
    'DP': int32
    'GQ': int32
    'PL': array<int32>
Column key:
    's': str
Row key:
    'locus': locus<GRCh37>
    'alleles': array<str>
Partition key:
    'locus': locus<GRCh37>

Common Operations

Like tables, Hail provides a number of useful methods for manipulating data in a matrix table.


MatrixTable has three methods to filter based on expressions:

Filter methods take a BooleanExpression argument. These expressions are generated by applying computations to the fields of the matrix table:

>>> filt_mt = mt.filter_rows(hl.len(mt.alleles) == 2)
>>> filt_mt = mt.filter_cols(hl.agg.mean(mt.GQ) < 20)
>>> filt_mt = mt.filter_entries(mt.DP < 5)

These expressions can compute arbitrarily over the data: the MatrixTable.filter_cols() example above aggregates entries per column of the matrix table to compute the mean of the GQ field, and removes columns where the result is smaller than 20.


MatrixTable has four methods to add new fields or update existing fields:

Annotate methods take keyword arguments where the key is the name of the new field to add and the value is an expression specifying what should be added.

The simplest example is adding a new global field foo that just contains the constant 5.

>>> mt_new = mt.annotate_globals(foo = 5)
>>> print(mt.globals.dtype.pretty())
struct {
    foo: int32

Another example is adding a new row field call_rate which computes the fraction of non-missing entries GT per row:

>>> mt_new = mt.annotate_rows(call_rate = hl.agg.fraction(hl.is_defined(mt.GT)))

Annotate methods are also useful for updating values. For example, to update the GT entry field to be missing if GQ is less than 20, we can do the following:

>>> mt_new = mt.annotate_entries(GT = hl.case()
...                                     .when(mt.GQ >= 20, mt.GT)
...                                     .or_missing())


Select is used to create a new schema for a dimension of the matrix table. For example, following the matrix table schemas from importing a VCF file (shown above), to create a hard calls dataset where each entry only contains the GT field one can do the following:

>>> mt_new = mt.select_entries('GT')
>>> print(mt_new.entry.dtype.pretty())
struct {
    GT: call

MatrixTable has four select methods that select and create new fields:

Each method can take either strings referring to top-level fields, an attribute reference (useful for accessing nested fields), as well as keyword arguments KEY=VALUE to compute new fields. The Python unpack operator ** can be used to specify that all fields of a Struct should become top level fields. However, be aware that all top-level field names must be unique. In this example, **mt[‘info’] would fail because DP already exists as an entry field.

The example below will keep the row keys locus and alleles as well as add two new fields: AC is making the subfield AC into a top level field and n_filters is a new computed field.

>>> mt_new = mt.select_rows(AC = mt.info.AC,
...                         n_filters = hl.len(mt['filters']))

The order of the fields entered as arguments will be maintained in the new matrix table.


The complement of select methods, MatrixTable.drop() can remove any top level field. An example of removing the GQ entry field is:

>>> mt_new = mt.drop('GQ')


Explode operations can is used to unpack a row or column field that is of type array or set.

One use case of explode is to duplicate rows:

>>> mt_new = mt.annotate_rows(replicate_num = [1, 2])
>>> mt_new = mt_new.explode_rows(mt_new['replicate_num'])
>>> mt.count_rows()
>>> mt_new.count_rows()
>>> mt_new.replicate_num.show()
| locus         | alleles    | replicate_num |
| locus<GRCh37> | array<str> |         int32 |
| 20:10019093   | ["A","G"]  |             1 |
| 20:10019093   | ["A","G"]  |             2 |
| 20:10026348   | ["A","G"]  |             1 |
| 20:10026348   | ["A","G"]  |             2 |
| 20:10026357   | ["T","C"]  |             1 |
| 20:10026357   | ["T","C"]  |             2 |
| 20:10030188   | ["T","A"]  |             1 |
| 20:10030188   | ["T","A"]  |             2 |
| 20:10030452   | ["G","A"]  |             1 |
| 20:10030452   | ["G","A"]  |             2 |


MatrixTable has three methods to compute aggregate statistics.

These methods take an aggregated expression and evaluate it, returning a Python value.

An example of querying entries is to compute the global mean of field GQ:

>>> mt.aggregate_entries(hl.agg.mean(mt.GQ))

It is possible to compute multiple values simultaneously (and encouraged, because grouping two computations together will run twice as fast!) by creating a tuple or struct:

>>> mt.aggregate_entries((agg.stats(mt.DP), agg.stats(mt.GQ)))
(Struct(mean=41.83915800445897, stdev=41.93057654787303, min=0.0, max=450.0, n=34537, sum=1444998.9999999995),
Struct(mean=67.73196915777027, stdev=29.80840934057741, min=0.0, max=99.0, n=33720, sum=2283922.0000000135))

See the Aggregators page for the complete list of aggregator functions.


Matrix tables can be aggregated along the row or column axis to produce a new matrix table.

First let’s add a random phenotype as a new column field case_status and then compute statistics about the entry field GQ for each grouping of case_status.

>>> mt_ann = mt.annotate_cols(case_status = hl.cond(hl.rand_bool(0.5),
...                                                 "CASE",
...                                                 "CONTROL"))

Next we group the columns by case_status and aggregate:

>>> mt_grouped = (mt_ann.group_cols_by(mt_ann.case_status)
...                 .aggregate(gq_stats = agg.stats(mt_ann.GQ)))
>>> print(mt_grouped.entry.dtype.pretty())
struct {
    gq_stats: struct {
        mean: float64,
        stdev: float64,
        min: float64,
        max: float64,
        n: int64,
        sum: float64
>>> print(mt_grouped.col.dtype)
struct{status: str}


Joins on two-dimensional data are significantly more complicated than joins in one dimension, and Hail does not yet support the full range of joins on both dimensions of a matrix table.

MatrixTable has methods for concatenating rows or columns:

MatrixTable.union_cols() joins matrix tables together by performing an inner join on rows while concatenating columns together (similar to paste in Unix). Likewise, MatrixTable.union_rows() performs an inner join on columns while concatenating rows together (similar to cat in Unix).

In addition, Hail provides support for joining data from multiple sources together if the keys of each source are compatible (same order and type, but the names do not need to be identical) using Python’s bracket notation []. The arguments inside the brackets are the destination key as a single value or a tuple if there are multiple destination keys.

For example, we can annotate rows with row fields from another matrix table or table. Let gnomad_data be a Table keyed by two row fields with type locus and array<str>, which matches the row keys of mt:

>>> mt_new = mt.annotate_rows(gnomad_ann = gnomad_data[mt.locus, mt.alleles])

If we only cared about adding one new row field such as AF from gnomad_data, we could do the following:

>>> mt_new = mt.annotate_rows(gnomad_af = gnomad_data[mt.locus, mt.alleles]['AF'])

To add all fields as top-level row fields, the following syntax unpacks the gnomad_data row as keyword arguments to MatrixTable.annotate_rows():

>>> mt_new = mt.annotate_rows(**gnomad_data[mt.locus, mt.alleles])

Interacting with Matrix Tables Locally

Some useful methods to interact with matrix tables locally are MatrixTable.describe(), MatrixTable.head(), and MatrixTable.sample(). describe prints out the schema for all row fields, column fields, entry fields, and global fields as well as the row keys, column keys, and the partition key. head returns a new matrix table with only the first N rows. sample returns a new matrix table where the rows are randomly sampled with frequency p.

To get the dimensions of the matrix table, use MatrixTable.count_rows() and MatrixTable.count_cols().


To save a matrix table to a file, use the MatrixTable.write(). These files can be read with read_matrix_table().

Linear Algebra

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Common errors

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Performance Considerations

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